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Lookup NU author(s): Dr Emma Brunton, Professor Kianoush Nazarpour
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The recording and analysis of peripheral neural signal can provide insight for various prosthetic and bioelectronics medicine applications. However, there are few studies that investigate how informative features can be extracted from population activity electroneurographic (ENG) signals. In this study, five feature extraction frameworks were implemented on sensory ENG datasets and their classification performance was compared. The datasets were collected in acute rat experiments where multi-channel nerve cuffs recorded from the sciatic nerve in response to proprioceptive stimulation of the hindlimb. A novel feature extraction framework, which incorporates spatio-temporal focus and dynamic time warping, achieved classification accuracies above 90% while keeping a low computational cost. This framework outperformed the remaining frameworks tested in this study and has improved the discrimination accuracy of the sensory signals. Thus, this study has extended the tools available to extract features from sensory population activity ENG signals. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
Author(s): Silveira C, Khushaba RN, Brunton E, Nazarpour K
Publication type: Article
Publication status: Published
Journal: Philosophical Transactions. Series A: Mathematical, Physical, and Engineering Sciences
Year: 2022
Volume: 380
Issue: 2228
Print publication date: 25/07/2022
Online publication date: 06/06/2022
Acceptance date: 08/11/2021
Date deposited: 23/06/2023
ISSN (print): 1364-503X
ISSN (electronic): 1471-2962
Publisher: Royal Society Publishing
URL: https://doi.org/10.1098/rsta.2021.0268
DOI: 10.1098/rsta.2021.0268
PubMed id: 35658682
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